A general consensus on representation of emotions and feelings in cognitive architectures is currently missing; yet artificial emotional intelligence is vital for the integration of future robots into the human society. This work introduces one possible approach to representation and processing of emotional mental states and attitudes, that allows for implementation of control of agent behavior by emotions as well as for recognition of emotional motivations in another agent's behavior. One particular advantage of this approach is that it allows for representation and processing of complex/social emotional attitudes, like shame, jealousy, resentment, or humor. The proposed validation of the approach is based on simulation of the emergence of emotional relationships in a small group of agents in a virtual environment.

Reaching for a target requires estimating the spatial position of the target and to convert such a position in a suitable arm-motor command. In the proposed framework, the location of the target is represented implicitly by the gaze direction of the robot and by the distance of the target. The NAO robot is provided with two cameras, one to look ahead and one to look down, which constitute two independent head-centered coordinate systems. These head-centered frames of reference are converted into reaching commands by two neural networks. The weights of networks are learned by moving the arm while gazing the hand, using an on-line learning algorithm that maintains the covariance matrix of weights. This work adapts a previously proposed model that worked on a full humanoid robot torso, to work with the NAO and is a step toward a more generic framework for the implicit representation of the peripersonal space in humanoid robots.

Smartphones incorporate many diverse and powerful sensors, which creates exciting new opportunities for data mining and human-computer interaction. In this paper we show how standard classification algorithms can use labeled smartphone-based accelerometer data to identify the physical activity a user is performing. Our main focus is on evaluating the relative performance of impersonal and personal activity recognition models. Our impersonal (i.e., universal) models are built using training data from a panel of users and are then applied to new users, while our personal models are built with data from each user and then applied only to new data from that user. Our results indicate that the personal models perform dramatically better than the impersonal models—even when trained from only a few minutes worth of data. These personal models typically even outperform hybrid models that utilize both personal and impersonal data. These results strongly argue for the construction of personal models whenever possible. Our research means that we can unobtrusively gain useful knowledge about the habits of potentially millions of users. It also means that we can facilitate human computer interaction by enabling the smartphone to consider context and this can lead to new and more effective applications.

The problem of designing and building effective assistive systems for human agents performing professional knowledge-intensive activities, or knowledge-works is of great interest and has wide implications. In this paper we propose a new approach for solving the problem. The approach is based on activity-context aware computation paradigm that can lead to flexible yet robust systems for holistic support in performing complex knowledge-works. To this end, we also outline here the notion of activity-context and the idea of activity-models as core artifacts used by such systems embodying the notion.

Knowledge workers work on many different tasks and must often switch between those tasks. In earlier work, we have shown the benefits of automatically capturing contexts for tasks for a specific category of knowledge worker, software programmers. Captured contexts facilitate task switches and reduce information overload by enabling the display of only the information relevant to the task-at-hand. In this paper, we describe the results of two studies of the use of captured contexts for a broad range of knowledge workers. The first study we describe is a field study of eight knowledge workers who used the model in their daily work for up to 25 days on tasks involving both file and web documents. We found that these knowledge workers need information to decay from their context and that our model is adequate at automatically trimming contexts. The second study is a case study of the use of contexts to support the operations of a software development company. We analyzed task contexts from hundreds of days of work from three users and found similar trends of information decaying from contexts. Results from each study also shed more light on the nature of mixed artifact task contexts.

Rapid development in the area of ambient intelligence introduced numerous applications. One of the fundamental underpinnings in such applications is an effective and reliable context-aware system able to recognize and understand activities performed by a human, and context in which it happened. However, there are two pending issues: (i) transferability, i.e., a specific implementation is tightly interrelated with a selected algorithm, available sensors, and a scenario/environment where they are employed; and (ii) comparability, i.e., there is no established benchmark problem that would enable a direct comparison of the developed context-aware systems. This paper first reviews some recent initiatives that address the abovementioned problems and then proposes a centralized collection of resources related to design and evaluation of context-aware systems. The main idea is to establish an online repository of datasets accompanied with the task, result and applied approach. Ideally, the contributors will provide the dataset with short description of the data, task and results, relevant paper, and link to resources such as implementation of the approach, preprocessing tools, and filtering. This would allow the community to quickly start building upon the latest state-of-the-art approaches, to benchmark newly developed techniques, and ultimately, to advance the frontiers in ambient intelligence.

We define and propose a manifesto and an architecture for smart digital workspaces and consumer playspaces, that “know” what the user is doing (activity structure, context, goals), how are they doing it (methods), what resources are they using (allocation and discovery), when (time) and where (location, application, device) are they doing it, who are they (profile, history), what is their role (responsibility, security, privacy) and who are their collaborators (social network), all the while observing, recording this context of work and play (institutional and social tribal knowledge). These smart workspaces and playspaces to be developed in the next five years, will let the users seamlessly move between applications and devices without having to remember or copy what they did earlier (activity context transfer and exchange), proactively show them steps others took in meaningfully similar situations before (semantic task reasoning), quickly find and show them directly related information and present answers to questions based on what they mean (proactive semantic extraction and search), in the context they need it, with access to provenance, quality and derivation of information, connect them to insights of experts within the organization and beyond, helping them reason and decide faster, with greater confidence, within a framework for managing, semantically dividing, tracking and enabling distributed work. We report two examples of the application of this architecture: a patient care system in a hospital and an assisted living system.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of three AI assignments from the 2012 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.

In an Artificial Intelligence course, a basic concept is Constraint Satisfaction (CS), which is acknowledged as a hard domain for teachers to teach and student to understand. In this paper, we present a game-based learning approach to assist students in learning CS algorithms, such as arc consistency and search algorithms, for problem solving in an easy, interactive and motivating way. Preliminary valuation has showed promising results.

We introduce a laboratory text on environmental and societal sustainability applications that can be a supplemental resource for any undergraduate AI course. The lab text, entitled Artificial Intelligence for Computational Sustainability: A Lab Companion, is brand new and incomplete; freely available through Wikibooks; and open to community additions of projects, assignments, and explanatory material on AI for sustainability. The project adds to existing educational efforts of the computational sustainability community, encouraging the flow of knowledge from research to education and public outreach. Besides summarizing the laboratory book, this paper touches on its implications for integration of research and education, for communicating science to the public, and other broader impacts.